Transit network design with allocation of green vehicles: A genetic algorithm approach

The use of fossil fuels in transportation generates harmful emissions that accounts for nearly half of the total pollutants in urban areas. Dealing with this issue, local authorities are dedicating specific efforts to seize the opportunity offered by new fuels and technological innovations in achieving a cleaner urban mobility. In fact, authorities are improving environmental performances of their public transport fleet by procuring cleaner vehicles, usually called low and zero emission vehicles (LEV and ZEV, respectively). Nevertheless there seems to be a lack of methodologies for supporting stakeholders in decisions related to the introduction of green vehicles, whose allocation should be performed since the network design process in order to optimize their available green capacity.In this paper, the problem of clean vehicle allocation in an existing public fleet is faced by introducing a method for solving the transit network design problem in a multimodal, demand elastic urban context dealing with the impacts deriving from transportation emissions.The solving procedure consists of a set of heuristics which includes a routine for route generation and a genetic algorithm for finding a sub-optimal set of routes with the associated frequencies.     

Dynamic demand estimation and microscopic traffic simulation of a university campus transportation network

Abstract

This article documents the authors' experience with the modeling, simulation, and analysis of a university transportation system, using the TRansportation ANalysis and SIMulation System (TRANSIMS). The processes of data preparation and network coding are described, followed by the algorithm developed to estimate the dynamic 24-hour demand, which includes a procedure for estimating the ‘desirability’ of the different parking lots from readily available data. The dynamic demand estimation algorithm is validated by comparing estimated and observed parking lot occupancies, where it is shown that the algorithm is capable of replicating observed results. Finally, an example is included to demonstrate how the developed model can be used in campus transportation planning. Besides serving as a first case study for using TRANSIMS to model a university campus, the study's contributions include the development of a procedure for parking lot desirability ranking and a practical procedure for estimating dynamic demand on university campuses.     

Flexible feeder transit route design to enhance service accessibility in urban area

To improve the accessibility of transit system in urban areas, this paper presents a flexible feeder transit routing model that can serve irregular‐shaped networks. By integrating the cost efficiency of fixed‐route transit system and the flexibility of demand responsive transit system, the proposed model is capable of letting operating feeder busses temporarily deviate from their current route so as to serve the reported demand locations. With an objective of minimizing total bus travel time, a new operational mode is then proposed to allow busses to serve passengers on both street sides. In addition, when multiple feeder busses are operating in the target service area, the proposed model can provide an optimal plan to locate the nearest one to response to the demands. A three‐stage solution algorithm is also developed to yield meta‐optimal solutions to the problem in a reasonable amount of time by transforming the problem into a traveling salesman problem. Numerical studies have demonstrated the effectiveness of the proposed model as well as the heuristic solution approach. Copyright © 2015 John Wiley & Sons, Ltd.     

“多管齐下,方能解忧”——对改善城市交通环境的思索

结合时代特征,从当前城市交通矛盾的两个主要方面——交通需求和交通供给展开分析,以解决目前特大、大型城市交通拥堵问题。     

基于进化博弈的城市中心区交通拥堵收费分析

交通拥堵收费是解决道路拥堵问题的有效措施。假设使用小汽车到达拥挤收费区域边界的出行者可通过2种方式到达拥挤收费中心区：小汽车直达及停车换乘公共交通（P＆R）。对于出行者甲（对小汽车出行需求偏刚性）、乙（对小汽车出行需求偏弹性）,采用进化博弈的方法,建立效益矩阵,分析在不同政府定价条件下两者出行方式选择行为的演化模型。结果表明,政府交通拥堵收费对出行者出行方式选择行为的演化起着重要作用,不同的定价区间会使得出行方式向不同的稳定状态演变。分析各种定价区间下出行者出行方式选择行为。     

Path flow estimator for planning applications in small communities

This paper presents an alternative planning framework to model and forecast network traffic for planning applications in small communities, where limited resources debilitate the development and applications of the conventional four-step travel demand forecasting model. The core idea is to use the Path Flow Estimator (PFE) to estimate current and forecast future traffic demand while taking into account of various field and planning data as modeling constraints. Specifically, two versions of PFE are developed: a base year PFE for estimating the current network traffic conditions using field data and planning data, if available, and a future year PFE for predicting future network traffic conditions using forecast planning data and the estimated base year origin–destination trip table as constraints. In the absence of travel survey data, the proposed method uses similar data (traffic counts and land use data) as a four-step model for model development and calibration. Since the Institute of Transportation Engineers (ITE) trip generation rates and Highway Capacity Manual (HCM) are both utilized in the modeling process, the analysis scope and results are consistent with those of common traffic impact studies and other short-range, localized transportation improvement programs. Solution algorithms are also developed to solve the two PFE models and integrated into a GIS-based software called Visual PFE. For proof of concept, two case studies in northern California are performed to demonstrate how the tool can be used in practice. The first case study is a small community of St. Helena, where the city’s planning department has neither an existing travel demand model nor the budget for developing a full four-step model. The second case study is in the city of Eureka, where there is a four-step model developed for the Humboldt County that can be used for comparison. The results show that the proposed approach is applicable for small communities with limited resources.     

基于神经网络的道路通行能力与交通需求匹配度判别研究

交通的供需是否匹配关系到城市和枢纽的发展前景。文中在探讨道路通行能力与需求匹配特性的基础上,用BP神经网络理论建立一种交通匹配预测模型。该模型发挥神经网络的优势,对数据并行处理和分布存储,通过训练、学习产生一个非线性映射,自适应地对数据进行预测。通过相关数据实验证明,该神经网络模型有较高的精度,并有较好的适用性。     

简化的能力谱方法在桥梁结构中的应用

为研究简化的能力谱方法的计算精度,通过一座连续桥梁的实际算例,给出简化能力谱方法的具体应用过程。建立简化横向单墩计算模型,用SAP2000有限元程序对模型进行推导分析,得到能力谱曲线;将三条不同地震记录对应的反应谱曲线转化为ADRS格式的需求谱曲线,并与得到的能力谱曲线绘于同一坐标系下,两种曲线的交点即为结构在相应地震动下的弹性响应,继而求得结构相应的非弹性响应。用非线性时程分析方法对同一计算模型及三条地震动加速度纪录进行分析,与采用简化的能力谱方法下得到的计算结果进行比较,从中看出:简化的能力谱方法可以用较少的计算工作量得到比较精确的结构非线性响应;在该方法中采用 μ T关系式,会得到不同精度的计算结果。不同的Rμ